Day-of-the-week effects in commercial paper yield rates

In order to minimize short-term financing costs, corporations issue commercial paper instead of seeking bank loans. We examine the changes in the daily rates of commercial paper over the last two decades. Our most interesting finding is based on a consistent and significant negative return on Wednesdays as compared to other weekdays over the sample period. We use t-tests, non-parametric tests and the binary regression developed by French [J. Finan. Econ. 8 (1) (1980) 55] to confirm our results. Finally, we deduct the return for each Wednesday from the average return for that week and find that Wednesday returns are significantly lower. Consistent with other money market instruments like T-bills and federal funds, we show that a day-of-the-week effect exists in the commercial paper rates.     

Generalized variance-ratio tests for serial correlation in multivariate regression models

Exact tests for rth order serial correlation in the multivariate linear regression model are devised which are based on a multivariate generalization of the F-distribution. The tests require the computation of two multivariate regressions. In the special case of a single-equation regression model the procedures reduce to simple always-conclusive F-tests. The tests are illustrated by applications to the Rotterdam Model of consumer demand.     

Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence

It is known that the small sample significance levels of Cox-type tests of non-nested regression models can be much greater than the nominal level. Adjustments designed to overcome this problem are discussed and two tests are proposed. Monte Carlo evidence on the performance of the tests derived in this paper, the Davidson-MacKinnon J-test and the Fisher-McAleer test is presented. The F-test applied to the comprehensive model is also included in the simulation experiments.     

M-tests in multivariate models

Summary For testing homogeneity in multivariatek sample model, robust tests based onM-estimators are proposed and their asymptoticx ²-distributions are investigated. FurthermoreM-tests in multivariate regression models are discussed.     

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

This paper develops a testing framework for comparing the predictive accuracy of competing multivariate density forecasts with different predictive copulas, focusing on specific parts of the copula support. The tests are framed in the context of the Kullback–Leibler Information Criterion, using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties for realistic sample sizes. In an empirical application to daily changes of yields on government bonds of the G7 countries we obtain insights into why the Student-t and Clayton mixture copula outperforms the other copulas considered; mixing in the Clayton copula with the t-copula is of particular importance to obtain high forecast accuracy in periods of jointly falling yields.     

Testing the autoregressive parameter with the t statistic

This paper considers a Gaussian first-order autoregressive process with unknown intercept where the initial value of the variable is a known constant. Monte Carlo simulations are used to investigate the sampling distribution of the t statistic for the autoregressive parameter when its value is in the neighborhood of unity. A small sigma asymptotic result is exploited in the construction of exact non-similar tests. The powers of non-similar tests of the random walk and other hypotheses are estimated for sample sizes typical in economic applications.     

Some tests for the constancy of regressions under heteroscedasticity

For testing the equality of coefficients of a linear regression model under heteroscedasticity, we suggest an F criterion conditioned on the posterior mean of the ratio of standard deviations of error terms in two subsamples. For pairable subsamples, and exact F test is derived. Sampling experiments show that the Chow test differs substantially from the nominal significance level when the two subsample sizes are unequal, and that the F test conditioned on the posterior mean is superior to other tests when sample sizes are small.     

The non-relevance of the elusive holy grail of asset pricing tests: The “true” market portfolio does not alter CAPM validity conclusions

Empirical evaluations of CAPM usually attach a caveat that rejection is conditional on the choice of market proxy. We explore the criticality of the proxy choice disclaimer. Using different proxies and comprehensive simulations of the unobserved “true” market in Fama–MacBeth tests of CAPM, we find that the significance (t-statistics) corresponding to betas remain consistently unaltered, even if the proxy is a small fraction of or has a low correlation with the true market. The constancy of t-statistics persists in a simulated true-CAPM world as well: if CAPM is indeed valid, the choice of proxy is unlikely to reject it erroneously. Identity of the elusive true market portfolio and the choice of representative proxy cannot overturn conclusions on validity of CAPM based on Fama–MacBeth tests. Roll’s Critique, incontrovertible in theory, may be quite forgiving in practice – CAPM cannot be resurrected by a “closer” approximation of the elusive true market portfolio when it has commonly been rejected.     

Estimation in a disequilibrium model and the value of information

Suits' model of the watermelon market is reformulated as a disequilibrium model and the likelihood function is derived under various assumptions concerning the amount of available information. Such models are characterized by a min condition as in y_t = min(D_t, S_t) for an ordinary demand-supply model, where the observed quantity y_t is the smaller demand and supply. Varying amounts of information may be available depending on which variables are observed and on whether prior information is available for separating the sample into subsets for which D_t < S_t and conversely. The likelihood function corresponding to the least amount of prior information is shown to be unbounded without a certain variance ratio restriction. Computations are successfully carried out both with the original model and Monte Carlo experiments and the effect of different amounts of information on MSE's is analyzed.     

Neglected heterogeneity in moment condition models

The central concern of this paper is parameter heterogeneity in models specified by a number of unconditional or conditional moment conditions and thereby the provision of a framework for the development of apposite optimal $m$-tests against its potential presence. We initially consider the unconditional moment restrictions framework. Optimal $m$-tests against moment condition parameter heterogeneity are derived with the relevant Jacobian matrix obtained in terms of the second order own derivatives of the moment indicator in a leading case. GMM and GEL tests of specification based on generalized information matrix equalities appropriate for moment-based models are described and their relation to optimal $m$-tests against moment condition parameter heterogeneity examined. A fundamental and important difference is noted between GMM and GEL constructions. The paper is concluded by a generalization of these tests to the conditional moment context and the provision of a limited set of simulation experiments to illustrate the efficacy of the proposed tests.     

Selecting local models in multiple regression by maximizing power

This paper considers multiple regression procedures for analyzing the relationship between a response variable and a vector of d covariates in a nonparametric setting where tuning parameters need to be selected. We introduce an approach which handles the dilemma that with high dimensional data the sparsity of data in regions of the sample space makes estimation of nonparametric curves and surfaces virtually impossible. This is accomplished by abandoning the goal of trying to estimate true underlying curves and instead estimating measures of dependence that can determine important relationships between variables. These dependence measures are based on local parametric fits on subsets of the covariate space that vary in both dimension and size within each dimension. The subset which maximizes a signal to noise ratio is chosen, where the signal is a local estimate of a dependence parameter which depends on the subset dimension and size, and the noise is an estimate of the standard error (SE) of the estimated signal. This approach of choosing the window size to maximize a signal to noise ratio lifts the curse of dimensionality because for regions with sparsity of data the SE is very large. It corresponds to asymptotically maximizing the probability of correctly finding nonspurious relationships between covariates and a response or, more precisely, maximizing asymptotic power among a class of asymptotic level αt-tests indexed by subsets of the covariate space. Subsets that achieve this goal are called features. We investigate the properties of specific procedures based on the preceding ideas using asymptotic theory and Monte Carlo simulations and find that within a selected dimension, the volume of the optimally selected subset does not tend to zero as n → ∞ unless the volume of the subset of the covariate space where the response depends on the covariate vector tends to zero.     

The exact moments of the least squares estimator for the autoregressive model

Exact mean and variance of the least squares estimate of the stationary first-order autoregressive coefficient, i.e., β in y_t=α+βx_t+u_t are evaluated algebraically as well as numerically. It turns out that the least squares estimate is seriously biased for the sample of two-digits sizes typically dealt with in econometrics if the mean of the process is unknown, i.e., if the equation has a non-zero intercept (α≠0). Kendall's approximation to the mean and Barlett's approximation to the variance are shown to be fairly good. Also, our numerical results confirm Orcutt and Winokur's (Econometrica, Vol. 37) based on Monte Carlo experiments.     

Efficient estimation and inference in linear pseudo-panel data models

We consider pseudo-panel data models constructed from repeated cross sections in which the number of individuals per group is large relative to the number of groups and time periods. First, we show that, when time-invariant group fixed effects are neglected, the OLS estimator does not converge in probability to a constant but rather to a random variable. Second, we show that, while the fixed-effects (FE) estimator is consistent, the usual t statistic is not asymptotically normally distributed, and we propose a new robust t statistic whose asymptotic distribution is standard normal. Third, we propose efficient GMM estimators using the orthogonality conditions implied by grouping and we provide t tests that are valid even in the presence of time-invariant group effects. Our Monte Carlo results show that the proposed GMM estimator is more precise than the FE estimator and that our new t test has good size and is powerful.     

Employee behavioral options in problematic working conditions: response pattern analysis

This study developed a decision tree framework to predict and distinguish employee responses to problematic working conditions. In this framework, job satisfaction and self-efficacy were antecedent variables; five responses from the modified EVLN typology were used as dependent variables. Data from 367 police officers in Taiwan were analyzed to examine the linear and quadratic relationships between job satisfaction and each of the five responses. The sample was further divided into six groups according to individual job satisfaction and self-efficacy. In each group, specific response patterns were analyzed through paired sample t-tests. The pattern analysis results partially supported the prediction of decision tree framework.     

Organization Promises in the Triangular Psychological Contract as Perceived by Temporary Agency Workers,Agencies, and Client Organizations

Temporary agency blue-collar workers (N = 100), the agencies through which they were hired (N = 12), and the client organizations to which they were assigned to work (N = 11) reported their perceptions on the content and the fulfillment or breach of organization promises within the psychological contract. All data were collected in Belgium through a survey from fall 2002 through spring 2003.Nonparametric tests and t-tests indicated similarity rather than discrepancy in the parties’ perceptions. Fulfillment of the psychological contract was the case rather than breach. When focusing on differences, agencies and client organizations perceived more promises made than their temporaries did. Agencies and client organizations had more favorable perceptions of their promises kept than their temporaries did. The temporaries perceived more promises made by their agencies than by their client organizations.     

A model of fractional cointegration, and tests for cointegration using the bootstrap

The paper proposes a framework for modelling cointegration in fractionally integrated processes, and considers methods for testing the existence of cointegrating relationships using the parametric bootstrap. In these procedures, ARFIMA models are fitted to the data, and the estimates used to simulate the null hypothesis of non-cointegration in a vector autoregressive modelling framework. The simulations are used to estimate p-values for alternative regression-based test statistics, including the F goodness-of-fit statistic, the Durbin–Watson statistic and estimates of the residual d. The bootstrap distributions are economical to compute, being conditioned on the actual sample values of all but the dependent variable in the regression. The procedures are easily adapted to test stronger null hypotheses, such as statistical independence. The tests are not in general asymptotically pivotal, but implemented by the bootstrap, are shown to be consistent against alternatives with both stationary and nonstationary cointegrating residuals. As an example, the tests are applied to the series for UK consumption and disposable income. The power properties of the tests are studied by simulations of artificial cointegrating relationships based on the sample data. The F test performs better in these experiments than the residual-based tests, although the Durbin–Watson in turn dominates the test based on the residual d.     

Residual based tests for cointegration in dependent panels

In the paper, we propose residual based tests for cointegration in general panels with cross-sectional dependency, endogeneity and various heterogeneities. The residuals are obtained from the usual least squares estimation of the postulated cointegrating relationships from each individual unit, and the nonlinear IV panel unit root testing procedure is applied to the panels of the fitted residuals using as instruments the nonlinear transformations of the adaptively   fitted lagged residuals. The t$t$-ratio, based on the nonlinear IV estimator, is then constructed to test for unit root in the fitted residuals for each cross-section. We show that such nonlinear IV t$t$-ratios are asymptotically normal and cross-sectionally independent under the null hypothesis of no cointegration. The average or the minimum of the IVt$t$-ratios can, therefore, be used to test for the null of a fully non-cointegrated panel against the alternative of a mixed panel, i.e., a panel with only some cointegrated units. We also consider the maximum of the IV t$t$-ratios to test for a mixed panel against a fully cointegrated panel. The critical values of the minimum, maximum as well as the average tests are easily obtained from the standard normal distribution function. Our simulation results indicate that the residual based tests for cointegration perform quite well in finite samples.     

Estimation in the first-order moving average model through the finite autoregressive approximation: Some asymptotic results

To estimate α in the model y_t = u_t+αu_t?1, we consider a proposal by Durbin (Biometrika, 1969). It consists in fitting an autoregression of order k to the data, and deriving from there an estimate α^. The probability limit and the variance of the limiting normal distribution of α^ are presented and discussed in detail, when the sample size T → ∞, but k remains fixed. The differences between the resulting values and those corresponding to the maximum likelihood estimator are exponentially decreasing functions of k. Several modifications of the estimator are discussed and found consistent, but asymptotically inefficient.     

The power of tests of predictive ability in the presence of structural breaks

This paper presents analytical, Monte Carlo, and empirical evidence on the effects of structural breaks on tests for equal forecast accuracy and encompassing. We show that out-of-sample predictive content can be hard to find because out-of-sample tests are highly dependent on the timing of the predictive ability. Moreover, predictive content is harder to find with some tests than others: in power, F-type tests of equal forecast accuracy and encompassing often dominate t-type alternatives. Based on these results and evidence from an empirical application, we conclude that structural breaks under the alternative may explain why researchers often find evidence of in-sample, but not out-of-sample, predictive content.     

On Tests for a Normal Mean with Known Coefficient of Variation

Hinkley (1977) derived two tests for testing the mean of a normal distribution with known coefficient of variation (c.v.) for right alternatives. They are the locally most powerful (LMP) and the conditional tests based on the ancillary statistic for μ. In this paper, the likelihood ratio (LR) and Wald tests are derived for the one‐ and two‐sided alternatives, as well as the two‐sided version of the LMP test. The performances of these tests are compared with those of the classical t, sign and Wilcoxon signed rank tests. The latter three tests do not use the information on c.v. Normal approximation is used to approximate the null distribution of the test statistics except for the t test. Simulation results indicate that all the tests maintain the type‐I error rates, that is, the attained level is close to the nominal level of significance of the tests. The power functions of the tests are estimated through simulation. The power comparison indicates that for one‐sided alternatives the LMP test is the best test whereas for the two‐sided alternatives the LR or the Wald test is the best test. The t, sign and Wilcoxon signed rank tests have lower power than the LMP, LR and Wald tests at various alternative values of μ. The power difference is quite large in several simulation configurations. Further, it is observed that the t, sign and Wilcoxon signed rank tests have considerably lower power even for the alternatives which are far away from the null hypothesis when the c.v. is large. To study the sensitivity of the tests for the violation of the normality assumption, the type I error rates are estimated on the observations of lognormal, gamma and uniform distributions. The newly derived tests maintain the type I error rates for moderate values of c.v.